# 自定义绘制ks曲线的函数
def plot_ks(y_test, y_score, positive_flag):
# 对y_test,y_score重新设置索引
y_test.index = np.arange(len(y_test))
#y_score.index = np.arange(len(y_score))
# 构建目标数据集
target_data = pd.DataFrame({'y_test':y_test, 'y_score':y_score})
# 按y_score降序排列
target_data.sort_values(by = 'y_score', ascending = False, inplace = True)
# 自定义分位点
cuts = np.arange(0.1,1,0.1)
# 计算各分位点对应的Score值
index = len(target_data.y_score)*cuts
scores = target_data.y_score.iloc[index.astype('int')]
# 根据不同的Score值，计算Sensitivity和Specificity
Sensitivity = []
Specificity = []
for score in scores:
# 正例覆盖样本数量与实际正例样本量
positive_recall = target_data.loc[(target_data.y_test == positive_flag) & (target_data.y_score>score),:].shape[0]
positive = sum(target_data.y_test == positive_flag)
# 负例覆盖样本数量与实际负例样本量
negative_recall = target_data.loc[(target_data.y_test != positive_flag) & (target_data.y_score<=score),:].shape[0]
negative = sum(target_data.y_test != positive_flag)
Sensitivity.append(positive_recall/positive)
Specificity.append(negative_recall/negative)
# 构建绘图数据
plot_data = pd.DataFrame({'cuts':cuts,'y1':1-np.array(Specificity),'y2':np.array(Sensitivity),
'ks':np.array(Sensitivity)-(1-np.array(Specificity))})
# 寻找Sensitivity和1-Specificity之差的最大值索引
max_ks_index = np.argmax(plot_data.ks)
plt.plot([0]+cuts.tolist()+[1], [0]+plot_data.y1.tolist()+[1], label = '1-Specificity')
plt.plot([0]+cuts.tolist()+[1], [0]+plot_data.y2.tolist()+[1], label = 'Sensitivity')
# 添加参考线
plt.vlines(plot_data.cuts[max_ks_index], ymin = plot_data.y1[max_ks_index],
ymax = plot_data.y2[max_ks_index], linestyles = '--')
# 添加文本信息
plt.text(x = plot_data.cuts[max_ks_index]+0.01,
y = plot_data.y1[max_ks_index]+plot_data.ks[max_ks_index]/2,
s = 'KS= %.2f' %plot_data.ks[max_ks_index])
# 显示图例
plt.legend()
# 显示图形
plt.show()
# 调用自定义函数，绘制K-S曲线
plot_ks(y_test = y_test, y_score = y_score, positive_flag = 1)
